Study of CT Images Processing with the Implementation of MLEM Algorithm using CUDA on NVIDIA’S GPU Framework

Authors

  • T. A. Valencia-Perez Faculty of Physical Sciences Mathematics Benemérita Universidad Autónoma de Puebla, Avenida San Claudio y 18 Sur, Colonia San Manuel, Building FM2-203, Ciudad Universitaria, C.P. 72570, Puebla, Mexico
  • J. M. Hernandez-Lopez Faculty of Physical Sciences Mathematics Benemérita Universidad Autónoma de Puebla, Avenida San Claudio y 18 Sur, Colonia San Manuel, Building FM2-203, Ciudad Universitaria, C.P. 72570, Puebla, Mexico
  • E. Moreno-Barbosa Faculty of Physical Sciences Mathematics Benemérita Universidad Autónoma de Puebla, Avenida San Claudio y 18 Sur, Colonia San Manuel, Building FM2-203, Ciudad Universitaria, C.P. 72570, Puebla, Mexico
  • B. de Celis-Alonso Faculty of Physical Sciences Mathematics Benemérita Universidad Autónoma de Puebla, Avenida San Claudio y 18 Sur, Colonia San Manuel, Building FM2-203, Ciudad Universitaria, C.P. 72570, Puebla, Mexico

DOI:

https://doi.org/10.15415/jnp.2020.72021

Keywords:

Computed tomography, Algorithms, GPU, Reconstruction, Image quality

Abstract

In medicine, the acquisition process in Computed Tomography Images (CT) is obtained by a reconstruction algorithm. The classical method for image reconstruction is the Filtered Back Projection (FBP). This method is fast and simple but does not use any statistical information about the measurements. The appearance of artifacts and its low spatial resolution in reconstructed images must be considered. Furthermore, the FBP requires of optimal conditions of the projections and complete sets of data. In this paper a methodology to accelerate acquisition process for CT based on the Maximum Likelihood Estimation Method (MLEM) algorithm is presented. This statistical iterative reconstruction algorithm uses a GPU Programming Paradigms and was compared with sequential algorithms in which the reconstruction time was reduced by up to 3 orders of magnitude while preserving image quality. Furthermore, they showed a good performance when compared with reconstruction methods provided by commercial software. The system, which would consist exclusively of a commercial laptop and GPU could be used as a fast, portable, simple and cheap image reconstruction platform in the future.

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Published

2020-02-28

How to Cite

(1)
Valencia-Perez, T. A. .; Hernandez-Lopez, J. M. .; Moreno-Barbosa, E. .; Celis-Alonso, B. de . Study of CT Images Processing With the Implementation of MLEM Algorithm Using CUDA on NVIDIA’S GPU Framework. J. Nucl. Phy. Mat. Sci. Rad. A. 2020, 7, 165-171.

Issue

Vol. 7 No. 2 (2020)

Section

Articles

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Articles in Journal of Nuclear Physics, Material Sciences, Radiation and Applications (J. Nucl. Phy. Mat. Sci. Rad. A.) by Chitkara University Publications are Open Access articles that are published with licensed under a Creative Commons Attribution- CC-BY 4.0 International License. Based on a work at http://jnp.chitkara.edu.in. This license permits one to use, remix, tweak and reproduction in any medium, even commercially provided one give credit for the original creation.

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